Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events

Autor: Alejandro Peña, Francisco Chiclana, Mario Gongora, Alejandro Patino, Fabio Caraffini, Juan David González-Ruiz, Eduardo Duque-Grisales
Rok vydání: 2021
Předmět:
Zdroj: Applied Soft Computing. 107:107381
ISSN: 1568-4946
Popis: Operational Risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technological failures and failures in the execution & administration of processes. However, due to the large amount of qualitative information, the uncertainty and the low frequency at which these risk events are generated in an organization, their modeling is still a technological challenge. This paper takes up this challenge and presents a fuzzy convolutional deep-learning model to estimate, based on the Basel III recommendations, the ORLoss Component (OR-LC) in an organization. The proposed model integrates qualitative information as linguistic random variables, as well as risk events data from different sources using multi-dimensional fuzzy credibility concepts. The results show the stability of the proposed model with respect to the OR-LC estimation from both structural and dimensional point of views, making it an ideal tool for modeling OR from the perspective of: (a) the regulators (Basel Committee on Banking Supervision) by allowing the integration of experts’ criteria into the OR-LC; (b) the insurers by allowing the integration of risk events from different sources; and (c) organizations and financial entities by allowing the a priori evaluation of the OR-LC of new financial products based on technological platforms and electronic channels.
Databáze: OpenAIRE
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